Millions of online users regularly post data and text to social media platforms, such as Twitter and Facebook, regarding a multitude of related and unrelated topics that include announcements, interests, hobbies, expressions, and many others. This proliferation of social media postings, and the variance and differences among the users' posting styles, languages, vernacular used by the millions of social media users creates a vast body of user-supplied social media data.
A problem arises for parties interested in connecting with or communicating with the social media users regarding specific categories of interest because of the large quantity of disparate data types and the differences in the data based on the multitude of posting styles, languages, and other personal conventions used by social media users. Conventional analytical and computational techniques are limited and fail to offer sufficient identification of such users.